Race, Risk, and the VBAC Calculator: The Politics of Race Correction in Childbirth

Why It Matters

The biased calculator steers Black patients toward repeat cesareans, increasing their exposure to surgical complications and worsening already stark maternal mortality gaps. Removing race from risk models is essential to prevent algorithmic reinforcement of health inequities.

Key Takeaways

• Original MFMU VBAC calculator included race, lowering Black patients' success odds
• Race correction justified by statistical association, not by underlying social factors
• Revised 2021 calculator removed race, highlighting broader issue of race-based risk tools
• Black women face three times higher maternal mortality, making VBAC predictions harmful
• Embedding race in risk models perpetuates structural inequities across multiple medical specialties

Pulse Analysis

The rise in U.S. cesarean deliveries over the past two decades—now about one in three births—has coincided with a steep decline in vaginal birth after cesarean (VBAC) attempts. Clinicians turned to the Maternal‑Fetal Medicine Units (MFMU) VBAC calculator, introduced in 2007, to quantify a patient’s chance of a successful vaginal delivery. By entering age, BMI, prior vaginal birth, indication for the first cesarean and, controversially, race and ethnicity, the tool produced a single percentage that quickly became a cornerstone of prenatal counseling.

Embedding race as a predictor gave the model a statistical edge but conflated correlation with causation. The lower success odds assigned to Black and Hispanic patients reflected historical disparities in hospital quality, access to continuous labor support, and the physiological toll of chronic stress—not innate biological differences. By presenting a race‑adjusted figure as objective fact, the calculator pre‑shaped the risk narrative before the patient could voice her preferences, effectively steering many Black women toward repeat cesareans and deepening the three‑fold maternal mortality gap.

The 2021 revision that stripped race and ethnicity from the VBAC calculator marked a pivotal step, yet it merely scratches the surface of a systemic problem. Similar race‑based adjustments persist in kidney function estimates, pulmonary testing, and cardiac risk scores, each translating social disadvantage into clinical exclusion. Eliminating these variables demands a shift toward models that incorporate concrete social determinants—such as insurance status, neighborhood resources, and provider bias—while discarding race as a proxy. Only then can obstetric care move from algorithmic inequity to truly patient‑centered decision making.